Today, we are going to help you find out the alternatives to TensorFlow. TensorFlow is a library that helps in machine learning and artificial intelligence, improving the quality of search engines. It is an end-to-end open-source, cross-platform library developed by the Google Brain team in 2015.
The importance of TensorFlow lies in situations where, when looking for automatic suggestions on a large scale, Google may not be able to fulfill your demand. This is where TensorFlow comes into the picture. The alternatives to TensorFlow are essentially deep learning libraries. In this article, we have prepared a list of the top 10 alternatives after a thorough market study.
Top 10 Alternatives to TensorFlow
Here, in this section, we are going to discuss the top 10 popular alternatives to TensorFlow. Let’s have a glance.
1. mlpack
mlpack is a machine learning software library with a focus on providing easy usage, scalability, and increased support for machine learning. It offers high flexibility and strong performance, making it popular among new users.
mlpack was released in 2008, with the latest stable version launched in June 2021. It provides template classes of GRU and LSTM structures, attracting both engineers and researchers.
2. Darknet
Darknet is an open-source library following a neural network framework. It utilizes C and CUDA programming languages, providing fast and easy installation. Darknet supports both CPU and GPU and is particularly helpful for research purposes. Though relatively new, it has gained popularity due to its quick installation process.
3. CatBoost
CatBoost is based on the decision tree library and is an open-source library developed by Yandex researchers in 2017. It supports Java, C++, and Python.
CatBoost is a fast, scalable, high-performance gradient booster library that also supports both CPU and GPU. Known for its accuracy, it reduces the need for extensive hyper-parameter tuning and allows easy customization of settings.
4. Theano
Theano is an open-source project developed by the University of Montreal, Quebec, with a BSD license. Written in Python and CUDA, it operates on Linux, macOS, and Microsoft Windows.
Theano optimizes the compilation of mathematical expressions, especially matrix calculations. While not easily learned directly, various projects and libraries, such as Made-Easy and Lasagne, provide support for Theano learners.
5. Keras
Keras is a Python-based open-source neural network library and is a crucial pick in our list. It can run on top of various libraries, including TensorFlow. Keras offers cognitive load reduction practices, and next-level extensibility, and supports GPU and TPU, especially in conjunction with CUDA. Its guiding principles and availability on Android and iOS make it attractive to users.
6. Torch
Torch is an open-source machine-learning framework designed for scientists. It uses the LUA programming language and has been licensed under BSD.
First marketed in 2002, Torch provides a wide range of deep learning algorithms, supporting scripting languages like LuaJIT and C. It offers a flexible N-dimensional array and wide GPU support.
7. Infer.NET
Released by Microsoft in 2008, Infer.NET is a model-based machine learning environment using C# and offering cross-platform support. It operates on Windows, macOS, and Linux, providing high-performance code frameworks for scalability, approximate determination, real-time data availability, and more. Licensed under MIT, the latest stable version was released in 2019.
8. Scikit Learn
Scikit Learn, released in 2007, is an open-source library based on Matplotlib. Developed by David Cournapeau, it is licensed under BSD. Focused more on data modeling than data loading and manipulation, Scikit Learn is based on Python and C++.
9. Training Mule
Training Mule simplifies the labeling of images and offers a set of databases for optimal results. It is a Java-based library, also known as Mulesoft training.
10. Cloud AutoML
Cloud AutoML is a machine learning service provided by Google, allowing developers with limited machine learning expertise to train high-quality models tailored to their business needs. It utilizes Google’s state-of-the-art transfer learning and architecture search technology.
Conclusion
We have mentioned all the popular alternatives in this article, each with its pros and cons. Choose the one that best fits your needs. We hope this information proves helpful to you.
